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'''
import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name_2_7B_instruct = "Zyphra/Zamba2-2.7B-instruct"
model_name_7B_instruct = "Zyphra/Zamba2-7B-instruct"
tokenizer_2_7B_instruct = AutoTokenizer.from_pretrained(model_name_2_7B_instruct)
model_2_7B_instruct = AutoModelForCausalLM.from_pretrained(
model_name_2_7B_instruct, device_map="cuda", torch_dtype=torch.bfloat16
)
tokenizer_7B_instruct = AutoTokenizer.from_pretrained(model_name_7B_instruct)
model_7B_instruct = AutoModelForCausalLM.from_pretrained(
model_name_7B_instruct, device_map="cuda", torch_dtype=torch.bfloat16
)
def extract_assistant_response(generated_text):
assistant_token = '<|im_start|> assistant'
end_token = '<|im_end|>'
start_idx = generated_text.rfind(assistant_token)
if start_idx == -1:
# Assistant token not found
return generated_text.strip()
start_idx += len(assistant_token)
end_idx = generated_text.find(end_token, start_idx)
if end_idx == -1:
# End token not found, return from start_idx to end
return generated_text[start_idx:].strip()
else:
return generated_text[start_idx:end_idx].strip()
def generate_response_2_7B_instruct(chat_history, max_new_tokens):
sample = []
for turn in chat_history:
if turn[0]:
sample.append({'role': 'user', 'content': turn[0]})
if turn[1]:
sample.append({'role': 'assistant', 'content': turn[1]})
chat_sample = tokenizer_2_7B_instruct.apply_chat_template(sample, tokenize=False)
input_ids = tokenizer_2_7B_instruct(chat_sample, return_tensors='pt', add_special_tokens=False).to(model_2_7B_instruct.device)
outputs = model_2_7B_instruct.generate(**input_ids, max_new_tokens=int(max_new_tokens), return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
"""
outputs = model_2_7B_instruct.generate(
input_ids=input_ids,
max_new_tokens=int(max_new_tokens),
do_sample=True,
use_cache=True,
temperature=temperature,
top_k=int(top_k),
top_p=top_p,
repetition_penalty=repetition_penalty,
num_beams=int(num_beams),
length_penalty=length_penalty,
num_return_sequences=1
)
"""
generated_text = tokenizer_2_7B_instruct.decode(outputs[0])
assistant_response = extract_assistant_response(generated_text)
return assistant_response
def generate_response_7B_instruct(chat_history, max_new_tokens):
sample = []
for turn in chat_history:
if turn[0]:
sample.append({'role': 'user', 'content': turn[0]})
if turn[1]:
sample.append({'role': 'assistant', 'content': turn[1]})
chat_sample = tokenizer_7B_instruct.apply_chat_template(sample, tokenize=False)
input_ids = tokenizer_7B_instruct(chat_sample, return_tensors='pt', add_special_tokens=False).to(model_7B_instruct.device)
outputs = model_7B_instruct.generate(**input_ids, max_new_tokens=int(max_new_tokens), return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
"""
outputs = model_7B_instruct.generate(
input_ids=input_ids,
max_new_tokens=int(max_new_tokens),
do_sample=True,
use_cache=True,
temperature=temperature,
top_k=int(top_k),
top_p=top_p,
repetition_penalty=repetition_penalty,
num_beams=int(num_beams),
length_penalty=length_penalty,
num_return_sequences=1
)
"""
generated_text = tokenizer_7B_instruct.decode(outputs[0])
assistant_response = extract_assistant_response(generated_text)
return assistant_response
with gr.Blocks() as demo:
gr.Markdown("# Zamba2 Model Selector")
with gr.Tabs():
with gr.TabItem("2.7B Instruct Model"):
gr.Markdown("### Zamba2-2.7B Instruct Model")
with gr.Column():
chat_history_2_7B_instruct = gr.State([])
chatbot_2_7B_instruct = gr.Chatbot()
message_2_7B_instruct = gr.Textbox(lines=2, placeholder="Enter your message...", label="Your Message")
with gr.Accordion("Generation Parameters", open=False):
max_new_tokens_2_7B_instruct = gr.Slider(50, 1000, step=50, value=500, label="Max New Tokens")
# temperature_2_7B_instruct = gr.Slider(0.1, 1.5, step=0.1, value=0.2, label="Temperature")
# top_k_2_7B_instruct = gr.Slider(1, 100, step=1, value=50, label="Top K")
# top_p_2_7B_instruct = gr.Slider(0.1, 1.0, step=0.1, value=1.0, label="Top P")
# repetition_penalty_2_7B_instruct = gr.Slider(1.0, 2.0, step=0.1, value=1.2, label="Repetition Penalty")
# num_beams_2_7B_instruct = gr.Slider(1, 10, step=1, value=1, label="Number of Beams")
# length_penalty_2_7B_instruct = gr.Slider(0.0, 2.0, step=0.1, value=1.0, label="Length Penalty")
def user_message_2_7B_instruct(message, chat_history):
chat_history = chat_history + [[message, None]]
return gr.update(value=""), chat_history, chat_history
def bot_response_2_7B_instruct(chat_history, max_new_tokens):
response = generate_response_2_7B_instruct(chat_history, max_new_tokens)
chat_history[-1][1] = response
return chat_history, chat_history
send_button_2_7B_instruct = gr.Button("Send")
send_button_2_7B_instruct.click(
fn=user_message_2_7B_instruct,
inputs=[message_2_7B_instruct, chat_history_2_7B_instruct],
outputs=[message_2_7B_instruct, chat_history_2_7B_instruct, chatbot_2_7B_instruct]
).then(
fn=bot_response_2_7B_instruct,
inputs=[
chat_history_2_7B_instruct,
max_new_tokens_2_7B_instruct
],
outputs=[chat_history_2_7B_instruct, chatbot_2_7B_instruct]
)
with gr.TabItem("7B Instruct Model"):
gr.Markdown("### Zamba2-7B Instruct Model")
with gr.Column():
chat_history_7B_instruct = gr.State([])
chatbot_7B_instruct = gr.Chatbot()
message_7B_instruct = gr.Textbox(lines=2, placeholder="Enter your message...", label="Your Message")
with gr.Accordion("Generation Parameters", open=False):
max_new_tokens_7B_instruct = gr.Slider(50, 1000, step=50, value=500, label="Max New Tokens")
# temperature_7B_instruct = gr.Slider(0.1, 1.5, step=0.1, value=0.2, label="Temperature")
# top_k_7B_instruct = gr.Slider(1, 100, step=1, value=50, label="Top K")
# top_p_7B_instruct = gr.Slider(0.1, 1.0, step=0.1, value=1.0, label="Top P")
# repetition_penalty_7B_instruct = gr.Slider(1.0, 2.0, step=0.1, value=1.2, label="Repetition Penalty")
# num_beams_7B_instruct = gr.Slider(1, 10, step=1, value=1, label="Number of Beams")
# length_penalty_7B_instruct = gr.Slider(0.0, 2.0, step=0.1, value=1.0, label="Length Penalty")
def user_message_7B_instruct(message, chat_history):
chat_history = chat_history + [[message, None]]
return gr.update(value=""), chat_history, chat_history
def bot_response_7B_instruct(chat_history, max_new_tokens):
response = generate_response_7B_instruct(chat_history, max_new_tokens)
chat_history[-1][1] = response
return chat_history, chat_history
send_button_7B_instruct = gr.Button("Send")
send_button_7B_instruct.click(
fn=user_message_7B_instruct,
inputs=[message_7B_instruct, chat_history_7B_instruct],
outputs=[message_7B_instruct, chat_history_7B_instruct, chatbot_7B_instruct]
).then(
fn=bot_response_7B_instruct,
inputs=[
chat_history_7B_instruct,
max_new_tokens_7B_instruct
],
outputs=[chat_history_7B_instruct, chatbot_7B_instruct]
)
if __name__ == "__main__":
demo.queue().launch()
'''
import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name_2_7B_instruct = "Zyphra/Zamba2-2.7B-instruct"
model_name_7B_instruct = "Zyphra/Zamba2-7B-instruct"
tokenizer_2_7B_instruct = AutoTokenizer.from_pretrained(model_name_2_7B_instruct)
model_2_7B_instruct = AutoModelForCausalLM.from_pretrained(
model_name_2_7B_instruct, device_map="cuda", torch_dtype=torch.bfloat16
)
tokenizer_7B_instruct = AutoTokenizer.from_pretrained(model_name_7B_instruct)
model_7B_instruct = AutoModelForCausalLM.from_pretrained(
model_name_7B_instruct, device_map="cuda", torch_dtype=torch.bfloat16
)
def extract_assistant_response(generated_text):
assistant_token = '<|im_start|> assistant'
end_token = '<|im_end|>'
start_idx = generated_text.rfind(assistant_token)
if start_idx == -1:
# Assistant token not found
return generated_text.strip()
start_idx += len(assistant_token)
end_idx = generated_text.find(end_token, start_idx)
if end_idx == -1:
# End token not found, return from start_idx to end
return generated_text[start_idx:].strip()
else:
return generated_text[start_idx:end_idx].strip()
def generate_response_2_7B_instruct(chat_history, max_new_tokens):
sample = []
for turn in chat_history:
if turn[0]:
sample.append({'role': 'user', 'content': turn[0]})
if turn[1]:
sample.append({'role': 'assistant', 'content': turn[1]})
chat_sample = tokenizer_2_7B_instruct.apply_chat_template(sample, tokenize=False)
input_ids = tokenizer_2_7B_instruct(chat_sample, return_tensors='pt', add_special_tokens=False).input_ids.to(model_2_7B_instruct.device)
# Handle context length limit
max_context_length = 4096
max_new_tokens = int(max_new_tokens)
max_input_length = max_context_length - max_new_tokens
if input_ids.size(1) > max_input_length:
input_ids = input_ids[:, -max_input_length:] # Truncate from the left (oldest tokens)
outputs = model_2_7B_instruct.generate(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
return_dict_in_generate=False,
output_scores=False,
use_cache=True,
num_beams=1,
do_sample=False
)
generated_text = tokenizer_2_7B_instruct.decode(outputs[0])
assistant_response = extract_assistant_response(generated_text)
return assistant_response
def generate_response_7B_instruct(chat_history, max_new_tokens):
sample = []
for turn in chat_history:
if turn[0]:
sample.append({'role': 'user', 'content': turn[0]})
if turn[1]:
sample.append({'role': 'assistant', 'content': turn[1]})
chat_sample = tokenizer_7B_instruct.apply_chat_template(sample, tokenize=False)
input_ids = tokenizer_7B_instruct(chat_sample, return_tensors='pt', add_special_tokens=False).input_ids.to(model_7B_instruct.device)
# Handle context length limit
max_context_length = 4096
max_new_tokens = int(max_new_tokens)
max_input_length = max_context_length - max_new_tokens
if input_ids.size(1) > max_input_length:
input_ids = input_ids[:, -max_input_length:] # Truncate from the left (oldest tokens)
outputs = model_7B_instruct.generate(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
return_dict_in_generate=False,
output_scores=False,
use_cache=True,
num_beams=1,
do_sample=False
)
generated_text = tokenizer_7B_instruct.decode(outputs[0])
assistant_response = extract_assistant_response(generated_text)
return assistant_response
with gr.Blocks() as demo:
gr.Markdown("# Zamba2 Model Selector")
with gr.Tabs():
with gr.TabItem("2.7B Instruct Model"):
gr.Markdown("### Zamba2-2.7B Instruct Model")
with gr.Column():
chat_history_2_7B_instruct = gr.State([])
chatbot_2_7B_instruct = gr.Chatbot()
message_2_7B_instruct = gr.Textbox(lines=2, placeholder="Enter your message...", label="Your Message")
with gr.Accordion("Generation Parameters", open=False):
max_new_tokens_2_7B_instruct = gr.Slider(50, 1000, step=50, value=500, label="Max New Tokens")
def user_message_2_7B_instruct(message, chat_history):
chat_history = chat_history + [[message, None]]
return gr.update(value=""), chat_history, chat_history
def bot_response_2_7B_instruct(chat_history, max_new_tokens):
response = generate_response_2_7B_instruct(chat_history, max_new_tokens)
chat_history[-1][1] = response
return chat_history, chat_history
send_button_2_7B_instruct = gr.Button("Send")
send_button_2_7B_instruct.click(
fn=user_message_2_7B_instruct,
inputs=[message_2_7B_instruct, chat_history_2_7B_instruct],
outputs=[message_2_7B_instruct, chat_history_2_7B_instruct, chatbot_2_7B_instruct]
).then(
fn=bot_response_2_7B_instruct,
inputs=[
chat_history_2_7B_instruct,
max_new_tokens_2_7B_instruct
],
outputs=[chat_history_2_7B_instruct, chatbot_2_7B_instruct]
)
with gr.TabItem("7B Instruct Model"):
gr.Markdown("### Zamba2-7B Instruct Model")
with gr.Column():
chat_history_7B_instruct = gr.State([])
chatbot_7B_instruct = gr.Chatbot()
message_7B_instruct = gr.Textbox(lines=2, placeholder="Enter your message...", label="Your Message")
with gr.Accordion("Generation Parameters", open=False):
max_new_tokens_7B_instruct = gr.Slider(50, 1000, step=50, value=500, label="Max New Tokens")
def user_message_7B_instruct(message, chat_history):
chat_history = chat_history + [[message, None]]
return gr.update(value=""), chat_history, chat_history
def bot_response_7B_instruct(chat_history, max_new_tokens):
response = generate_response_7B_instruct(chat_history, max_new_tokens)
chat_history[-1][1] = response
return chat_history, chat_history
send_button_7B_instruct = gr.Button("Send")
send_button_7B_instruct.click(
fn=user_message_7B_instruct,
inputs=[message_7B_instruct, chat_history_7B_instruct],
outputs=[message_7B_instruct, chat_history_7B_instruct, chatbot_7B_instruct]
).then(
fn=bot_response_7B_instruct,
inputs=[
chat_history_7B_instruct,
max_new_tokens_7B_instruct
],
outputs=[chat_history_7B_instruct, chatbot_7B_instruct]
)
if __name__ == "__main__":
demo.queue().launch()
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